Video signals are often disturbed by noise. The most frequent type of noise is frame noise, also called analogue noise, which is furthermore just called noise in this invention. (Other noise types would be frame noise, block noise or mosquito noise, which are both not addressed here.) This noise can come from analogue or digital photography, where it may originate from short exposure times or photo sensor properties. It can also come from bad signal storage or transmission, for example if the signal was deteriorated and amplified many times.
This noise is usually uniformly distributed over the whole image. It may be correlated to some properties of the image, like brightness or contrast, but may also be uncorrelated, which is not directly relevant for this invention. It is most easily visible in static, uniform parts of an image sequence, where small deviations of color or brightness become visible. It is often desired to know the level of noise to either give feedback to the user, or to control some other image processing like sharpness enhancement or noise reduction.
Simple state of the art methods, as described, for instance, in http://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio, compare a reference sequence with the same noisy sequence. By calculating the pixel-wise difference between the images, then summing up the difference and normalizing the result, a noise level can be obtained.
State of the art reference-free methods, as described, for instance, in US 2008/0232458 A1, measures noise in uniform areas of the image. Usually in the era of analogue television, a known black area outside the visible part of the image was evaluated. Since the content of the area was known to be black, the difference from the black level can be measured, summed, normalized and used as noise level. Nowadays, video sequences may not have such a known black area anymore, so that this method can not be used anymore as often and as easily as in earlier times.
US 2005/128355 A1 discloses a method of removing noise from digital moving picture data. It is proposed to reduce the number of frames used in a temporal filtering operation, which enables to detect motion between frames easily. The method comprises a method of spatial filtering, a method of temporal filtering, and a method of performing the spatial filtering and the temporal filtering sequentially. The spatial filtering method applies a spatial filtering in a YCbCr color space, preserving a contour/edge in the image in the spatial domain, and generating a weight that is adaptive to the noise for discriminating the contour/edge in the temporal filtering operation. The temporal filtering method applies temporal filtering based on motion detection and scene change detection, compensating for global motion, the motion detection considering the brightness difference and color difference of the pixels compared between frames in the temporal filtering operation, and a weight that is adaptive to the noise for detecting the motion in the temporal filtering operation. The spatial filtering method is preferably performed first, and the temporal filtering method is performed with the result of the spatial filtering.